1. Field
Exemplary embodiments of the present invention relate to a device for removing a noise on an image using a cross-kernel type median filter and a method therefor, and more particularly, a device for removing a noise on an image using a cross-kernel type median filter and a method therefor that improves an image quality and a compression efficiency and acquires an improved signal to noise ratio by solving a structural problem of a conventional median filter having a low image quality in an edge or a corner region of an image using a cross-kernel type median filter.
2. Description of the Related Art
In general, an image data is transferred and processed via a public television network or a cable network. Since a data transferring or processing step of an image data is exposed to various type noises, a noise is necessarily added to an original image, and the noise added to the image data deteriorates an image quality or resolution of a reproduced image.
Since a signal to noise ratio is lowered in a low illuminance, a noise may be increased relatively to an original image, an image may not be recognized, and a subjective and visual satisfaction may be deteriorated by restoring a color, which does not exist in a signal processing step.
Meanwhile, since the signal to noise ratio is increased in a high illuminance, a subjective satisfaction is increased but an absolute value of a noise caused by a shot noise is increased.
A photographed image is compressed by a standard compression technique such as a joint photographic experts group (JPEG) or H.264. If a noise exists in an image, a negative effect that deteriorates a compression efficiency to a same image quality occurs. Thus, a process for removing the noise on the image is necessary to improve a subjective image quality and a compression efficiency.
The noise on the image may be classified into a noise on a light and darkness signal and a noise on a chroma signal. An optic nerve of a person includes rods for recognizing the light and darkness and cones for recognizing colors. At least one hundred million rods are distributed on a peripheral region of retina and perform a function of a fast black-and-white film. Millions of cones are densely distributed on a central region of the retinal and recognize colors under a sufficient brightness.
The rods determines whether the objects are visible or invisible. The rods have been developed to recognize the objects under a dark place or an intensive light. However, the colors indicate not a physical amount but a psychological sense, and are resulted from the eyes of people to distinguish the recognized objects through the light and darkness. Thus, the people are sensitive to the change of the light and darkness but are insensitive to the change of the colors.
Since the noise on the chroma signal is deviated from the nature, which is sensed by the people, people feel sensitively unnatural and uncomfortable. However, the nerve cells have been evolved such that people feel sensitively the minute change of the light and darkness. Thus, since the noise on the light and darkness is natural relatively, people feel naturally the noise on the light and darkness even if the noise on the light and darkness is a noise.
However, it is very important to remove the noise on the light and darkness and the chroma. In an image compression technique such as the JPEC or the H.264/H.265, the noise on the light and darkness deteriorates the compression efficiency and increases the size of the compressed image. Since this low compression efficiency causes a noise such as a blocking artifact in a reproduced image, it is more important to remove the noise appropriately.
A median filter may remove a noise with a high resolution on a boundary region. Thus, the median filter has been widely used to remove a noise on an image.
It is necessary to maintain a high resolution on a main outline of an object and remove only the noise on the image. It is a basic theory of the noise removing to remove a fast change of the image by acquiring an average value or a median based on the data, which are neighbored spatially. Since it is more efficient for the noise removing to acquire the median instead of the average value, the median filter has been widely used, and a noise removing algorithm includes two steps generally.
In a first step, it is determined whether a current processing pixel is a noise or not. A normal standard is to calculate a signal complexity on a predetermined region on a basis of the current processing pixel. The variance is widely used as a calculation method of the signal complexity.
If a position of a horizontal direction is denoted as ‘i’, a position of a vertical direction is denoted as ‘j’, a signal value is denoted as x(i,j), a horizontal direction region of the noise removing is denoted as ‘i−N to i+N’, and a vertical direction region of the noise removing is denoted as ‘j−N to j+N’, the variance σ2 will be described as below with reference to the equation 1.
            E      ⁡              [        X        ]              =                            1                                    (                                                2                  ⁢                  N                                +                1                            )                        2                          ⁢                              ∑                          =                              -                N                                      N                    ⁢                                    ∑                              =                                  -                  N                                            N                        ⁢                          x              ⁡                              (                                                      i                    +                    m                                    ,                                      j                    +                    n                                                  )                                                        =      μ                  E      ⁡              [                  X          2                ]              =                  1                              (                                          2                ⁢                N                            +              1                        )                    2                    ⁢                        ∑                      =                          -              N                                N                ⁢                              ∑                          =                              -                N                                      N                    ⁢                                    x              ⁡                              (                                                      i                    +                    m                                    ,                                      j                    +                    n                                                  )                                      2                                                  ⁢                                                      σ              2                        =                        ⁢                          E              ⁡                              [                                                      (                                          X                      -                      μ                                        )                                    2                                ]                                                                                      =                        ⁢                          E              ⁡                              [                                                      X                    2                                    -                                      2                    ⁢                    μ                    ⁢                                                                                  ⁢                    X                                    +                                      μ                    2                                                  ]                                                                                      =                        ⁢                                          E                ⁡                                  [                                      X                    2                                    ]                                            -                              2                ⁢                                                                  ⁢                μ                ⁢                                                                  ⁢                                  E                  ⁡                                      [                    X                    ]                                                              +                              μ                2                                                                                      =                        ⁢                                          E                ⁡                                  [                                      X                    2                                    ]                                            -                              2                ⁢                                  μ                  ·                  μ                                            +                              μ                2                                                                                      =                        ⁢                                          E                ⁡                                  [                                      X                    2                                    ]                                            -                              μ                2                                                          ⁢                
If the variance is determined as above, it is determined whether a noise can be removed or not on a target region by comparing a reference value with a predetermined reference value. Since the variance has a large value when the outline is included in the target region, the main outline may be protected by determining the noise removing in a case of the variance having a small value. If the noise removing is determined, the average value or the median on a predetermined target region on a basis of the current processing pixel is determined as a value where the noise is removed.
The average value indicates not the noise removing but the size reduction on the image. However, since the median indicates that a remarkable value is removed by comparing the neighbor values from each other, the efficiency of the noise removing is prominent. For example, if a current data value is 100 and values of four neighboring data are zero, since the current data value is prominent due to the difference between the current data value and the four neighboring data values, the current data is regarded as the noise and is removed.
That is, in this case, if the average value is acquired, the current data value is reduced from 100 to 20, and a noise reduction effect occurs. But, in this case, if the median is acquired, the current data value is reduced from 100 to 0, and a noise removing effect occurs.
FIG. 1 illustrates a method for removing a noise in accordance with a conventional technique.
Referring to FIG. 1, a central pixel 100 of 3×3 region is a target pixel on which a noise will be removed. That is, the target pixel on which the noise will be removed is disposed on a center instead of a corner 3×3 region.
When 10 bit data is assumed, a black rectangle shown in FIG. 1 represents a pixel having data value of a low bit ranged from 0 to 500, and a white rectangle shown in FIG. 1 represents a pixel having data value of an upper bit ranged from 600 to 1023.
Herein, if the average value is used, the noise removing effect may be not performed since the data may be not included in any one of two regions. However, if the median is used, the noise removing may be performed with the outline since the data are included in any one of two regions without determining whether the data is a noise or not.
That is, in case that the target pixel on which the noise will be removed is disposed on the center instead of the corners of two regions, it is possible to remove the noise through the median filter using a conventional method.
FIGS. 2A and 2B illustrate problems of the method for removing noise on an image in accordance with a conventional technique.
Referring to FIG. 2A, although a target pixel 200 on which a noise will be removed are disposed between two regions, if the target pixel 200 is disposed on a corner of one of two regions, the result is shown as FIG. 2B due to a characteristic of a median filter, which is determined by a majority value. That is, if the target pixel 200 is disposed on a corner between two regions, a noise removing or a noise reduction does not occur and a relevant image is removed.
FIGS. 3A and 3B illustrate other problems of a method for removing a noise removing a noise on an image in accordance with a conventional technique.
Referring to FIGS. 3A and 3B, in case that target pixels 300 on which a noise will be removed are disposed to have a thin line type on a region, an error may occur when the median is used. That is, although the target pixels 300 are determined as a portion of the relevant image, the target pixels 300 are removed since the target pixels 300 are not included in the majority within the kernel used in the median calculation.
Since the median filter is a most useful tool for removing a noise, the median filter has been used as a basic tool in a noise removing algorithm. Also, the median filter may remove a noise efficiently if it is determined reasonably whether the data is a noise or not. Thus, most noise removing algorithms removes the noise by using two steps of determining whether the data is a noise or not and then, removing the noise.
However, as described above, since an image quality is deteriorated on a sharp edge or a corner in a median filter, only the median filter itself may be not used for removing the noise.